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Mediterranean climate‐type regions (MCRs) are characterised by warm‐to‐hot dry summers and mild‐wet winters. These regions are typically found on the western or southern edges of continents, for example, in the Mediterranean Basin, the west coast of North and South America, southern Africa and southwest Australia. The MCRs are vulnerable to climate variability and change related to their unique characteristics, such as pronounced rainfall seasonality and prolonged hot and dry summers. Based on historical observations and CMIP6 climate projections, we apply an empirical bio‐climatic assessment of how the geographic distribution of MCRs has changed during the last century and how these zones will be further impacted under continued warming. Results indicate a poleward and eastward expansion of MCRs in the Mediterranean Basin, North America‐California and South America‐Central Chile regions. For parts of Southern Africa and Southern Australia, a retreat of the MCR margins and an expansion of more arid climate zones are projected. These shifts are particularly profound according to high emission and radiative forcing pathways and future scenarios. The warming in MCRs is projected to accelerate (e.g., mean regional warming of up to 5.5°C under a 4°C global warming scenario), and precipitation will decrease by about 5%–10% for every additional degree of global warming. One exception is the California MCR, where rainfall will likely increase. Such changes can challenge water resources, food security and other aspects of human livelihood and ecosystems in these unique geographical zones. [ABSTRACT FROM AUTHOR]

The present study assesses the climate of the Middle East and North Africa (MENA) in detail, focusing on the historical and future warming trends in the region. The assessment incorporates data from observations, reanalyses, and statistically downscaled climate models from the Coupled Model Intercomparison Project Phases 5 and 6. Since the pre‐industrial era, the observed average climate in the MENA has warmed by 1.5°C and is on the brink of exceeding 2°C. The reanalysis data suggest that the regional warming over some MENA sub‐regions is three times faster than the global average. By the end of the 21st century, the Arabian Peninsula is projected to warm to 2.6°C ± 0.57°C and 7.6°C ± 1.53°C under low and high greenhouse gas emission scenarios, respectively. Distinct warming hotspots emerge over the Arabian Peninsula and Algeria in summer and over Mauritania in West Africa and the Elburz Mountains in Iran in winter. The summer hotspot over the Arabian Peninsula has already warmed by more than 2°C and can potentially warm to approximately 9°C under the high‐emission scenario. As global warming progresses to 1.5, 2.0, 3.0, and 4.0°C, the average temperature over the MENA land is projected to increase by 2.3°C ± 0.18°C, 3.0°C ± 0.22°C, 4.6°C ± 0.26°C, and 6.1°C ± 0.31°C, respectively. The 1.5, 2.0, 3.0, and 4.0°C warming levels over the MENA are expected to predate those of the global mean by two or three decades. Natural climate fluctuations also significantly influence the region's warming, contributing to temperature extremes. Plain Language Summary: Warming of the Earth's climate due to anthropogenic influences is expected to continue in the future. The present study examines in detail the actual and projected temperature changes in the Middle East and North Africa (MENA) region from 1850 to the end of this century. Using the most recently available data and models, we show that the region, notably the Arabian Peninsula, is warming significantly faster than the rest of the world. The high warming rate in the central part of the Arabian Peninsula is comparable to that in the Arctic and is two to three times the global average. This finding is concerning because the Arabian Peninsula is already one of the hottest locations on Earth. Additional warming hotspots are Algeria, Mauritania, and the Elburz Mountains in Iran. Rapid warming in the MENA will considerably affect the population and may compromise habitability. Key Points: Middle East and North Africa are warming much faster than the global average, with notable hotspots in the Arabian Peninsula and AlgeriaMiddle East and North Africa will reach 1.5, 2, 3, and 4°C warming levels two to three decades earlier than the global averageBy 2100, high emissions could warm parts of the Arabian Peninsula by 9°C, posing severe environmental and societal challenges [ABSTRACT FROM AUTHOR]

The urgency of understanding the intricate input–output relationships of the hydrologic cycle is amplified by the accelerating climate change impacts in mountain environments. This study focuses on optimising water resource management of a dammed valley in the Central Alps (Northern Italy). The research aims to integrate radar data and precipitation interpolation techniques (TIN, Copula, cumulative distribution function; CDF techniques, inverse distance weighting; IDW, thin plate spline; TPS, ordinary kriging; OK and detrended kriging; DK) into a semi‐distributed hydrologic model, by utilising hourly precipitation data from 22 rain gauges and a composite weather radar product spanning 2010–2020. Two main objectives were pursued: (i) to develop and evaluate various radar precipitation correction methods against a benchmark dataset and (ii) to calibrate and assess the performance of the GEOFrame hydrologic model forced with corrected precipitation input. Point‐based and spatial correction approaches were evaluated against ground measurements through leave‐one‐out tests. The former derives dependence functions between the biased radar series and those of the closest three rain gauges to the target point applying a triangular irregular network. The latter combines deterministic and geospatial interpolations to the rain gauge/radar residuals to derive a corrected surface by incorporating radar values as trends. Precipitation series exceeding the composite scaled score of the benchmark dataset were used as input for hydrologic modelling. The spatial method combining radar values with ordinary kriging provided the best results for both correction and modelling (hourly KGE > 0.75). The spatial approaches proved easier to apply than the point‐based methods. In addition, correcting precipitation significantly improved low‐flow simulation from negative hourly lnNSE to values greater than 0.25. As a further step, given the overall good performance of the spatial methods, they could be used operationally as an ensemble to analyse management scenarios. [ABSTRACT FROM AUTHOR]

Climate models are useful tools for analyzing historical and projecting future climate conditions. However, the model results tend to differ systematically from observations, particularly for parameters with complex spatial and temporal distributions such as precipitation. A combination of quantile mapping and generalized additive models (GAMs) is presented and proposed as a new method (Q‐GAM) for the bias correction of daily precipitation. Q‐GAM is demonstrated by using data from five European stations with different climate characteristics. For each station, the closest continental grid point of a EURO‐CORDEX climate model was selected for bias correction. A bootstrapping experiment is presented with over 1000 repetitions of randomly splitting the historical period 1981 to 2005 into a calibration and evaluation period. The results for all stations reveal that Q‐GAM is a straightforward, accurate and computationally efficient method for the bias correction of precipitation. In particular, the method improves the frequency of dry days and the total annual rainfall amount. This outcome is robust across stations with varying climate characteristics and also to the choice of calibration and evaluation periods. Similar results are also obtained for other precipitation characteristics such as the 0.9 and 0.95 quantiles. [ABSTRACT FROM AUTHOR]

Precipitation holds significant importance as a climate parameter in various applications, including studies on the impacts of climate change. However, its simulation or projection accuracy is low, primarily due to its high stochasticity. Specifically, climate models often overestimate the frequency of light rainy days while simultaneously underestimating the total amounts of extreme observed precipitation. This phenomenon, known as "drizzle bias", specifically refers to the model's tendency to overestimate the occurrence of light precipitation events. Consequently, even though the overall precipitation totals are generally well represented, there is often a significant bias in the number of rainy days. The present study aims to minimize the drizzle bias in model output by developing and applying two statistical approaches. In the first approach, the number of rainy days is adjusted based on the assumption that the relationship between observed and simulated rainy days remains the same in time (thresholding). In the second, a machine learning method (random forest or RF) is used for the development of a statistical model that describes the relationship between several climate (modelled) variables and the observed number of wet days. The results demonstrate that employing a multivariate approach yields results that are comparable to the conventional thresholding approach when correcting sub-periods with similar climate characteristics. However, the importance of utilizing RF becomes evident when addressing periods exhibiting extreme events, marked by a significantly distinct frequency of rainy days. These disparities are particularly pronounced when considering higher temporal resolutions. Both methods are illustrated on data from three EURO-CORDEX climate models. The two approaches are trained during a calibration period, and they are applied for the selected evaluation period. [ABSTRACT FROM AUTHOR]